As AI moves beyond language into understanding the visual world, demand for high-quality video data is exploding. Every major AI company is racing to train “world models” that can see, reason, and act — and they all need vast amounts of rights-cleared visual content to do it.
This article breaks down what’s actually happening inside these models — how they learn, why video is now the most valuable training data, and where studios like yours fit in. If you’ve ever wondered how your archive could power the next generation of AI (and get paid for it), this is the place to start.
Think of an AI model like a baby’s brain.
When a baby is born, their brain doesn’t know anything — it just starts taking in the world. They hear words, see faces, touch things, and little by little, their brain figures out how stuff fits together.
AI models learn in kind of the same way. Instead of hearing “mama” and “dada,” they get fed billions of words, sentences, pictures, and videos. They look for patterns — what comes next, what goes together, what means what.
At the very start of training — what researchers call time zero — the model begins with billions (sometimes trillions) of tiny adjustable “knobs,” known as parameters, all set to random values. During training, the model is shown a piece of text and asked to predict what comes next. At first, its guesses are completely wrong — like a baby babbling nonsense. But with each example, the parameters are adjusted slightly to make the next guess a little better.
Over time, these adjustments accumulate until the model becomes remarkably good at predicting the next word or image feature — effectively “learning” the structure of language, visuals, and meaning itself. And the more data it sees about a particular topic, the better it becomes at producing accurate, relevant output on that subject.
The First Models: Learning to Talk
The first big AIs were trained on words — huge piles of text from the internet. They read everything: Wikipedia, books, blogs, code, and news articles.
From all that reading, they learned the structure of language — grammar, logic, even humor. That’s why models like ChatGPT can write essays, tell jokes, or help you code. They don’t “understand” like people do, but they’ve seen so many examples that they can guess what comes next with scary accuracy.
The same is true for programming languages — AI models have read enormous amounts of code, so they’ve learned the structure and patterns of how developers write. That’s why AI can now act as a powerful coding assistant, predicting your next line or helping fix errors almost instantly.
If you think about it, it’s like a kid who’s read the whole internet — and all of GitHub too. You ask them to write a poem or debug your code, and they go, “Oh yeah, I’ve seen a thousand examples of that before.
The Next Models: Learning to See
Now we’re moving into the next phase — world models. If the first generation of AI learned to talk, the next ones are learning to see.
Here’s why that’s huge:
The stuff we see — images, video, movement, the physical world — contains far more information than what we read or hear. Every frame of a video or photo is packed with detail: light, texture, faces, motion, emotion. By the time a child turns four, their brain has absorbed millions of visual scenes — faces, gestures, colors, motion — far more raw information than they’ll ever read in words. It’s how they can tell a cat from a dog, or Mom from Dad, long before they can describe them.
AI is trying to do the same thing — to learn from the known world.
But here’s the key difference: humans live in the physical world, constantly absorbing sensory input from birth — sight, sound, touch, motion. That real-world grounding helps us generalize fast. A human can learn to drive in a few hours because they already understand depth, motion, and cause and effect.
AI models, on the other hand, don’t have that lived experience. They have to learn everything from scratch, through data alone. To reach the same understanding, a model might need millions of hours of driving footage just to predict what action to take next. That means teaching models to recognize what’s in a scene, understand what’s happening, and predict what might come next.
What They Need to Learn the World
To build these “world models,” AI companies need two main ingredients:
- Massive computer power (called “compute”) to do the learning.
- Massive amounts of video data to learn from.
Think of it like trying to teach a robot how to see and move through life. You’d have to show it everything — cities, forests, faces, hands, traffic, sports, animals, conversations — millions of hours of it. That’s the kind of visual understanding these new models are chasing.
Every tech company in the world — OpenAI, Google, Anthropic, Meta, ByteDance — is in a race to build the most complete “world model.” Because once you have a model that can truly understand how the world looks and works, you can build anything on top of it — from robots that can help in hospitals to AIs that can edit films or train other AIs.
But here’s what’s becoming clear: data is the real moat. Every lab trains on roughly the same hardware — the same Nvidia chips, the same types of compute. And while one group might make a breakthrough in algorithms, those advances quickly spread as others adopt and refine them. Over time, the learning methods will converge.
What won’t converge is the data. The unique, high-quality data each company trains on will be the true competitive edge — the difference between a model that merely imitates the world and one that actually understands it.
That's Where Versos Fits In
We help supply the video side of this equation — the visual data that teaches AI what the world actually looks like. Not random YouTube clips scraped off the web, but licensed, rights-cleared, high-quality footage.
It’s like giving the next generation of AI its eyes — safely, ethically, and with a full chain of custody. Get in touch today to learn more.